Identifying myself as an expat has largely shaped my personality, perspective, and aspiration. Over the past 10 years, I have garnered extensive expertise in research, coding, mentoring, and fostering collaborations with diverse academic and industrial partners in Europe and the U.S. This multifaceted experience allowed me to learn a robust problem-solving approach to face challenges at work, including three fundamental steps: understanding and visualizing problems quickly, developing and implementing innovative solutions, and evaluating and optimizing the outcomes. This strategy have been pivotal in my professional success and personal development. I continuously developed my skills and stayed up-to-date with the latest advancements in computer science, biomedical engineering, and digital health.

My background is in digital health and artificial intelligence, and my mission is to address challenges in digital healthcare, from multimodal data collection to extracting actionable insights and delivering personalized interventions, by leveraging artificial intelligence, health data processing, and wearable Internet-of-Things technologies. My expertise spans developing data analysis techniques – such as signal processing and machine learning – in mobile health applications. For the past 7 years ago, my job responsibilities have included the following:

  • Research: I have acquired theoretical knowledge in a wide range of topics, such as Artificial Intelligence, Internet of Things, health data analytics, large language models, wearable electronics, fog computing, and digital health. My research has been focused on developing data analysis methods for biomedical signals, e.g., photoplethysmogram (PPG) and electrocardiogram (ECG). I have been developing personalized machine-learning-based health data analytic techniques (e.g., CNN- LSTM- and GAN-based), application of LLMs in healthcare, anomaly event detection methods for longitudinal / multivariate data, and signal quality assessment techniques, to mention a few. I am the co-creator of openCHA, a foundational Large Language Models (LLMs)-powered framework, enabling Conversational Health Agents (CHAs) to deliver effective healthcare services, including patient support, health coaching, and promoting patient self-awareness. In my research, I have been collaborating with multidisciplinary teams at the University of California Irvine, Stanford University, iHealth Labs, University of Turku, Vienna University of Technology, and Silo AI, to mention a few. I am the author of more than 70 peer-reviewed publications, both in medical and technological venues.
  • Coding: I have developed my coding skills (mostly in Python since 2015) by taking an active role in different projects including health data collection (e.g., PPG and ECG) and wearable- and cloud-based health data analysis techniques. I gained experience working with various Python libraries, including TensorFlow, Scikit-learn, SciPy, and Pandas. My experience in programming also consists of developing wearables apps in C and mobile apps using Kotlin.
  • Mentoring: I have served as an advisor to master’s and Ph.D. students, focusing on the development of AI solutions for healthcare applications. My primary objective is to mentor students and junior researchers, helping them enhance their expertise in signal processing and machine learning techniques and improve their critical thinking and problem-solving abilities.
  • Teaching: I was the teacher of two courses as “Acquisition and Analysis of Biosignals” and “Biosignal Analytics” for three years, where I taught the fundamentals of signal processing and machine learning techniques used for biomedical signals and health data analysis.

Research Area & Interests

  • mHealth
  • LLM Agents
  • Wearable-based Health Monitoring and Coaching
  • Machine Learning
  • Biomedical signal Processing